We study the effect of different types of 3D point convolutions by training our model using the deep parametric continuous convolution (PCC) [1]} and the FKAConv [2]}. The results are provided in Table REF . The comparison with the PCC shows that the FKAConv module reduces the network size by \(\sim 5\%\)  while slightly improves the performance by \(\sim 3\%\) . We also trained our model without the 3D branch and the performance dropped considerably as shown in Table REF .
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